size <- 12. Later this will be the number of rows of the matrix.x <- rnorm( size ).x1 by adding (on average 10 times smaller) noise to x: x1 <- x + rnorm( size )/10.x and x1 should be close to 1.0: check this with function cor.x2 and x3 by adding (other) noise to x.size <- 12
x <- rnorm( size )
x1 <- x + rnorm( size )/10
cor( x, x1 )
[1] 0.9951966
x2 <- x + rnorm( size )/10
x3 <- x + rnorm( size )/10
x1, x2 and x3 column-wise into a matrix using m <- cbind( x1, x2, x3 ).m.m.heatmap( m, Colv = NA, Rowv = NA, scale = "none" ).m <- cbind( x1, x2, x3 )
class( m )
[1] "matrix"
head( m )
x1 x2 x3
[1,] 0.8670266 1.0086217 0.9910694
[2,] -1.0661894 -0.7442766 -0.8370095
[3,] 0.2589836 0.2543197 0.1402526
[4,] -0.4362874 -0.4915745 -0.3127264
[5,] 0.5950119 0.5528441 0.6696093
[6,] -0.1546422 -0.1053228 -0.1559430
heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow
# x1, x2, x3 follow similar color pattern, they should be correlated
y1…y4 (but not correlated with x), of the same length size.m from columns x1…x3,y1…y4 in some random order.y <- rnorm( size )
y1 <- y + rnorm( size )/10
y2 <- y + rnorm( size )/10
y3 <- y + rnorm( size )/10
y4 <- y + rnorm( size )/10
m <- cbind( y4, y3, x2, y1, x1, x3, y2 )
heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow
cc <- cor( m ) to build the matrix of correlation coefficients between columns of m.round( cc, 3 ) to show this matrix with 3 digits precision.cc <- cor( m )
round( cc, 3 ) #
y4 y3 x2 y1 x1 x3 y2
y4 1.000 0.997 0.106 0.995 0.154 0.097 0.996
y3 0.997 1.000 0.092 0.993 0.142 0.084 0.994
x2 0.106 0.092 1.000 0.115 0.991 0.995 0.104
y1 0.995 0.993 0.115 1.000 0.167 0.110 0.993
x1 0.154 0.142 0.991 0.167 1.000 0.993 0.153
x3 0.097 0.084 0.995 0.110 0.993 1.000 0.094
y2 0.996 0.994 0.104 0.993 0.153 0.094 1.000
heatmap( cc, symm = TRUE, scale = "none" )
# E.g. value for (row: x1, col: y1) is the corerlation of vectors x1, y1.
# Values of 1.0 are on the diagonal: e.g. x1 is perfectly correlated with x1.
# Correlations between x, x vectors are close to 1.0.
# Correlations between y, y vectors are close to 1.0.
# Correlations between x, y vectors are close to 0.0.
Copyright © 2021 Biomedical Data Sciences (BDS) | LUMC